{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TC Landfall:\n",
      "     Grids       Used      Min     P25       P50      P75     Max\n",
      "[['74572.00' '70641.00' '1.00' '5.00' '17.50' '52.00' '145.25']\n",
      " ['16070.00' '15744.00' '1.00' '12.50' '35.75' '72.50' '168.00']\n",
      " ['12269.00' '12087.00' '1.00' '20.25' '68.25' '155.00' '368.50']\n",
      " ['8781.00' '8735.00' '1.00' '11.25' '39.75' '62.75' '140.50']\n",
      " ['11544.00' '11534.00' '1.00' '15.50' '30.25' '65.25' '139.75']\n",
      " ['4276.00' '4276.00' '1.00' '5.00' '16.75' '57.31' '131.25']]\n",
      "TCP Contribution:\n",
      "     Min           P25           P50          P75         Max\n",
      "[['77188.00' '69982.00' '0.00' '0.10' '0.58' '2.34' '8.40']\n",
      " ['16498.00' '15841.00' '0.00' '0.26' '1.13' '3.21' '8.69']\n",
      " ['12704.00' '11685.00' '0.00' '0.69' '2.26' '4.67' '13.53']\n",
      " ['8849.00' '8561.00' '0.00' '0.29' '1.41' '3.37' '8.52']\n",
      " ['12233.00' '11672.00' '0.00' '0.93' '3.16' '8.18' '21.22']\n",
      " ['4348.00' '4229.00' '0.00' '0.24' '1.09' '5.06' '14.22']]\n",
      "Total Intensity:\n",
      "     Grids       Used      Min     P25       P50      P75     Max\n",
      "[['77188.00' '75802.00' '2.98' '5.87' '7.30' '8.99' '13.90']\n",
      " ['16498.00' '16388.00' '3.03' '6.16' '7.41' '8.83' '12.89']\n",
      " ['12704.00' '12675.00' '4.05' '6.84' '8.30' '9.97' '14.70']\n",
      " ['8849.00' '8594.00' '3.09' '6.45' '8.87' '10.69' '17.35']\n",
      " ['12233.00' '11758.00' '4.18' '7.06' '8.18' '9.07' '12.40']\n",
      " ['4348.00' '4167.00' '4.48' '6.61' '7.26' '8.10' '10.57']]\n",
      "TCP Intensity:\n",
      "     Grids       Used      Min     P25       P50      P75     Max\n",
      "[['77188.00' '76577.00' '1.03' '7.64' '12.91' '17.57' '32.69']\n",
      " ['16498.00' '16437.00' '1.31' '8.81' '13.70' '16.99' '29.32']\n",
      " ['12704.00' '12580.00' '1.26' '10.93' '15.28' '19.54' '32.79']\n",
      " ['8849.00' '8820.00' '1.23' '9.92' '14.59' '19.18' '33.19']\n",
      " ['12233.00' '11720.00' '2.97' '13.59' '17.02' '20.25' '31.15']\n",
      " ['4348.00' '4343.00' '1.29' '10.70' '15.80' '20.90' '36.22']]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Fig ED4\n",
    "\n",
    "# Import libraries\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import loadtxt\n",
    "\n",
    "from my_functions import remove_outliers_IQR\n",
    "from my_functions import get_stats_all_data\n",
    "from my_functions import get_stats_without_outliers\n",
    "\n",
    "fig, ax = plt.subplots(1,4, figsize=(12, 8),sharex=False,sharey=False)\n",
    "fig.tight_layout(pad=2)\n",
    "plt.subplots_adjust(bottom=0.08)\n",
    "plt.subplots_adjust(left=0.1)\n",
    "#ax.set_ylim(bottom=0, top=5)\n",
    "idir='DATA/FIG_ED4/'\n",
    "xlabels=['GLOB', 'NCA', 'EAS', 'SWA', 'OCE', 'EAF']\n",
    "\n",
    "data_1 = loadtxt(idir+'TC_EVENTS_GRID_GLOBAL')\n",
    "data_2 = loadtxt(idir+'TC_EVENTS_GRID_NCA')\n",
    "data_3 = loadtxt(idir+'TC_EVENTS_GRID_EAS')\n",
    "data_4 = loadtxt(idir+'TC_EVENTS_GRID_SWA')\n",
    "data_5 = loadtxt(idir+'TC_EVENTS_GRID_OCE')\n",
    "data_6 = loadtxt(idir+'TC_EVENTS_GRID_EAF')\n",
    "\n",
    "print(\"TC Landfall:\")\n",
    "print(\"     Grids       Used      Min     P25       P50      P75     Max\")\n",
    "data = [data_1, data_2, data_3, data_4, data_5, data_6]\n",
    "stats_without_outliers = get_stats_without_outliers(data)\n",
    "formatted_data = np.vectorize(lambda x: f\"{x:.2f}\")(stats_without_outliers)\n",
    "print(formatted_data)\n",
    "\n",
    "data_1=remove_outliers_IQR(data_1)\n",
    "data_2=remove_outliers_IQR(data_2)\n",
    "data_3=remove_outliers_IQR(data_3)\n",
    "data_4=remove_outliers_IQR(data_4)\n",
    "data_5=remove_outliers_IQR(data_5)\n",
    "data_6=remove_outliers_IQR(data_6)\n",
    "data = [data_1, data_2, data_3, data_4, data_5, data_6]\n",
    "\n",
    "#sns.violinplot(ax= ax[0], data=data,color=None,fill=True,inner=\"box\",cut=0,palette=\"Set2\")\n",
    "sns.boxplot(ax= ax[0],data=data,whis=[0, 100])\n",
    "ax[0].tick_params(axis='x', rotation=45)\n",
    "ax[0].yaxis.grid(True)\n",
    "ax[0].axhline(0, color='red',linewidth=0.8,linestyle='--')\n",
    "ax[0].set_xticklabels(xlabels)\n",
    "ax[0].set_title('A) TC landfalls',loc='left')\n",
    "ax[0].set_ylabel('[Events]')\n",
    "ax[0].set_xlabel('Basins')\n",
    "#ax[0].set_ylim([0, 400])\n",
    "\n",
    "data_1 = loadtxt(idir+'TCP_CONTRIBUTION_GRID_GLOBAL')\n",
    "data_2 = loadtxt(idir+'TCP_CONTRIBUTION_GRID_NCA')\n",
    "data_3 = loadtxt(idir+'TCP_CONTRIBUTION_GRID_EAS')\n",
    "data_4 = loadtxt(idir+'TCP_CONTRIBUTION_GRID_SWA')\n",
    "data_5 = loadtxt(idir+'TCP_CONTRIBUTION_GRID_OCE')\n",
    "data_6 = loadtxt(idir+'TCP_CONTRIBUTION_GRID_EAF')\n",
    "\n",
    "print(\"TCP Contribution:\")\n",
    "print(\"     Min           P25           P50          P75         Max\")\n",
    "data = [data_1, data_2, data_3, data_4, data_5, data_6]\n",
    "stats_without_outliers = get_stats_without_outliers(data)\n",
    "formatted_data = np.vectorize(lambda x: f\"{x:.2f}\")(stats_without_outliers)\n",
    "print(formatted_data)\n",
    "\n",
    "data_1=remove_outliers_IQR(data_1)\n",
    "data_2=remove_outliers_IQR(data_2)\n",
    "data_3=remove_outliers_IQR(data_3)\n",
    "data_4=remove_outliers_IQR(data_4)\n",
    "data_5=remove_outliers_IQR(data_5)\n",
    "data_6=remove_outliers_IQR(data_6)\n",
    "data = [data_1, data_2, data_3, data_4, data_5, data_6]\n",
    "\n",
    "\n",
    "\n",
    "#sns.violinplot(ax= ax[1], data=data,color=None,fill=True,inner=\"box\",cut=0,palette=\"Set2\")\n",
    "sns.boxplot(ax= ax[1],data=data,whis=[0, 100])\n",
    "ax[1].tick_params(axis='x', rotation=45)\n",
    "ax[1].yaxis.grid(True)\n",
    "ax[1].axhline(0, color='red',linewidth=0.8,linestyle='--')\n",
    "ax[1].set_xticklabels(xlabels)\n",
    "ax[1].set_title('B) TCP Contribution',loc='left')\n",
    "ax[1].set_ylabel('[%]')\n",
    "ax[1].set_xlabel('Basins')\n",
    "#ax[1].set_ylim([-1, 25])\n",
    "\n",
    "data_1 = loadtxt(idir+'TOTAL_INTENSITY_DAY_GRID_GLOBAL')\n",
    "data_2 = loadtxt(idir+'TOTAL_INTENSITY_DAY_GRID_NCA')\n",
    "data_3 = loadtxt(idir+'TOTAL_INTENSITY_DAY_GRID_EAS')\n",
    "data_4 = loadtxt(idir+'TOTAL_INTENSITY_DAY_GRID_SWA')\n",
    "data_5 = loadtxt(idir+'TOTAL_INTENSITY_DAY_GRID_OCE')\n",
    "data_6 = loadtxt(idir+'TOTAL_INTENSITY_DAY_GRID_EAF')\n",
    "\n",
    "print(\"Total Intensity:\")\n",
    "print(\"     Grids       Used      Min     P25       P50      P75     Max\")\n",
    "data = [data_1, data_2, data_3, data_4, data_5, data_6]\n",
    "stats_without_outliers = get_stats_without_outliers(data)\n",
    "formatted_data = np.vectorize(lambda x: f\"{x:.2f}\")(stats_without_outliers)\n",
    "print(formatted_data)\n",
    "\n",
    "data_1=remove_outliers_IQR(data_1)\n",
    "data_2=remove_outliers_IQR(data_2)\n",
    "data_3=remove_outliers_IQR(data_3)\n",
    "data_4=remove_outliers_IQR(data_4)\n",
    "data_5=remove_outliers_IQR(data_5)\n",
    "data_6=remove_outliers_IQR(data_6)\n",
    "data = [data_1, data_2, data_3, data_4, data_5, data_6]\n",
    "\n",
    "\n",
    "\n",
    "#sns.violinplot(ax= ax[2], data=data,color=None,fill=True,inner=\"box\",cut=0,palette=\"Set2\")\n",
    "sns.boxplot(ax= ax[2],data=data,whis=[0, 100])\n",
    "ax[2].tick_params(axis='x', rotation=45)\n",
    "ax[2].yaxis.grid(True)\n",
    "ax[2].axhline(0, color='red',linewidth=0.8,linestyle='--')\n",
    "ax[2].set_xticklabels(xlabels)\n",
    "ax[2].set_title('C) Total PPT Intensity',loc='left')\n",
    "ax[2].set_ylabel('[mm/day]')\n",
    "ax[2].set_xlabel('Basins')\n",
    "ax[2].set_ylim([0, 40])\n",
    "\n",
    "data_1 = loadtxt(idir+'TC_INTENSITY_DAY_GRID_GLOBAL')\n",
    "data_2 = loadtxt(idir+'TC_INTENSITY_DAY_GRID_NCA')\n",
    "data_3 = loadtxt(idir+'TC_INTENSITY_DAY_GRID_EAS')\n",
    "data_4 = loadtxt(idir+'TC_INTENSITY_DAY_GRID_SWA')\n",
    "data_5 = loadtxt(idir+'TC_INTENSITY_DAY_GRID_OCE')\n",
    "data_6 = loadtxt(idir+'TC_INTENSITY_DAY_GRID_EAF')\n",
    "\n",
    "print(\"TCP Intensity:\")\n",
    "print(\"     Grids       Used      Min     P25       P50      P75     Max\")\n",
    "data = [data_1, data_2, data_3, data_4, data_5, data_6]\n",
    "stats_without_outliers = get_stats_without_outliers(data)\n",
    "formatted_data = np.vectorize(lambda x: f\"{x:.2f}\")(stats_without_outliers)\n",
    "print(formatted_data)\n",
    "\n",
    "data_1=remove_outliers_IQR(data_1)\n",
    "data_2=remove_outliers_IQR(data_2)\n",
    "data_3=remove_outliers_IQR(data_3)\n",
    "data_4=remove_outliers_IQR(data_4)\n",
    "data_5=remove_outliers_IQR(data_5)\n",
    "data_6=remove_outliers_IQR(data_6)\n",
    "data = [data_1, data_2, data_3, data_4, data_5, data_6]\n",
    "\n",
    "\n",
    "\n",
    "#sns.violinplot(ax= ax[2], data=data,color=None,fill=True,inner=\"box\",cut=0,palette=\"Set2\")\n",
    "sns.boxplot(ax= ax[3],data=data,whis=[0, 100])\n",
    "ax[3].tick_params(axis='x', rotation=45)\n",
    "ax[3].yaxis.grid(True)\n",
    "ax[3].axhline(0, color='red',linewidth=0.8,linestyle='--')\n",
    "ax[3].set_xticklabels(xlabels)\n",
    "ax[3].set_title('D) TCP Intensity',loc='left')\n",
    "ax[3].set_ylabel('[mm/day]')\n",
    "ax[3].set_xlabel('Basins')\n",
    "ax[3].set_ylim([0, 40])\n",
    "\n",
    "# show plot\n",
    "#plt.show()\n",
    "\n",
    "plt.savefig('FIG_EXT/Fig_ED4.png', dpi=300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
